Keen eyes will notice tiny improvements in the look-and-feel of the Discover blogs today, thanks to behind-the-scenes work of our crack website team. One improvement is that the social-media buttons at the bottom of each post are a little more clear and logical. They also let you know how many people have passed along a post via each medium.

Which leads me to an entirely unoriginal observation: the internet loves Top Ten lists. Perusing our home page, it’s easy to be struck by the giant numbers for the Things Everyone Should Know About Time post. It’s true that I like to think the post was actually interesting. (People seem to be divided between whether #4 or #10 is the most striking entry.)

But still, I’ll be honest: being at the conference I hadn’t been able to blog much, so I thought it would be good to write something that would be popular but not too hard to write. Thus: a top ten list. Box office!

So why exactly is that? I’m not disparaging: a good list is a way to convey a substantial amount of information in a well-organized form. But still, would it have been as popular had it been Top Seven? What if each entry were three times as long? What if the exact same words were presented without the numbers and bold-face labels?

Here are the top four reasons we like lists:
1. We like easy answers without having to think much about them.
2. Lists sound official.
3. We have ten fingers and toes, so the number ten appeals to us.
4. It makes it easier for us to pick things we like and throw away the things we don’t like. For example, I completely disregarded #2 in your list because it was certainly incorrect

Torbjörn Larsson, OM

Which leads me to an entirely unoriginal observation: the internet loves Top Ten lists.

All good and well. But how will I know which list to read without having a Top Ten list of “Top Ten lists”!?

I feel there is something amiss here.

Joe Shobe

The list is like a roller coaster. You expect peaks and valleys but at least one crescendo of a moment. We love to see things get better and more exciting. And the writer almost always saves the best for last. Great entertainment, and you usually learn something. The Time list was sensational. Quite the ride. And relax about #2 , it simply says “I can sure tell where this is going because I know full well where it’s been.”

Bill

I always knew:

You have the cool clear
Eyes of a seeker of wisdom and truth,

Yet, there’s that up turned chin
And the grin of impetuous youth.

Oh, I believe in you,
I believe in you.

But who is answering the question:

How can you be two places at once when you’re not anywhere at all? (to the tune of Alice’s Restaurant)

http://backreaction.blogspot.com/ Bee

I don’t like these lists. Just cheap writing. I forget immediately what was on them. See also:

I think it also had to do with the item appearing on (at least my version of) the Google news home page. I don’t know if it got there because it was already popular, whether it was picked by an algorithm or by a person, but once it was there it was sure to become huge. I had already read it, from my regular visits to the blog, when I noticed it on Google news, and was very pleased to see you getting the attention.

http://christiandrugrehab.us/ Simon

No one likes to read paragraphs anyone – lists are nice because they are easier to skim and they condense information into understandable bits.

If you want to write for the internet I think it is a necessity that you write in this manner.

http://terrybollinger.com Terry Bollinger

That’s what I get for getting bored and taking a peek back at this blog…

This simple question of the value of top-ten lists touches in surprising ways on questions of what intelligence is and how it works. E.g., I would say that it has unexpected relevance to the technical problem of how one might construct a low-power, physically compact unit, say insect or mouse sized, that is capable of “interesting” autonomous behaviors such as general survival and the carrying out of specific goals. Through that connection you have touched a bit on my day job.

Top ten lists are a good idea because they make explicit what is otherwise implicit.

Any illusion that intelligence is based on a full analysis of every factor involved is just that — an illusion. Biological intelligence is most conspicuously different from classic machine intelligence for how incredibly time and energy efficient it is at discarding information, far more than for how well it combines the information that remains after that discarding process. It’s the glance across the room, a mere few hundred pixels over as little at a tenth of a second, that conveys critical data with potentially life-altering implications, not the orders of magnitudes of less critical data streams that must in that same instant be discarded to find that datum.

Borrowing a bit of terminology from mathematical physics, at least part of this process seems to rely on a more generalizes and adaptive version of the idea of basis sets — that is, of parsing up huge sets of data into predefined “patterns” that together are capable moment by moment of reconstructing a pretty good model of what the outside world. The basis set of Fourier transforms is simply the set of sinusoidals, from which any curve can be constructed. The data compression industry long ago found out that other less mathematically precise basis sets are capable of providing remarkable levels of both compression and accuracy, and therein lies a truly fascinating story if you dig into it deeply enough.

Image and data compression works for one reason only: We live in a world of mixed entropy levels, which is to say a world of complexity that exhibits threads and patterns that repeat in the most fantastically complex ways, yet not so complex that we lose all access to those threads and patterns. Such a loss is what happens in the extremum of very high heat, which is in a very real way a world dominated by information. Alas, the rich information of extreme heat has become so complex and mixed together that attempting to hold onto some uniquely meaningful bit of information within it is literally like trying to corral a drop of blue-dyed water after it has fallen into a blender set on frappe. (Yet this inaccessibility through complexity itself becomes a new and distinctive pattern, one we call temperature, provided only that less-complex patterns still exist from which to observe and name regions of extremely high temperature.)

Biological systems seem to have glommed onto the potential of mixed-complexity systems in ways that we still do not understand well. We can discard information — and here “we” most definitely includes such simple neurological systems as those of insects and other “simple” animals — in no small part because we’ve developed ways to model most of it using simple, low-cost ways of representing them — that is, by using data compressions. But what is most striking about how biological systems do this is how extraordinarily little data they can process to recognize the existence of an appropriate pattern. Humans do it all the time without realizing it, and so do other animals. I have been struck by how several universities that work with small wing-flapping robots these days have encountered a shared problem: Hawks, which like to take out their robots. Why? Because to a hawk, visual data that conveys a certain flapping motion is pulled out very quickly by fast, energy-efficient motion detection cells in the retina, and certain combination of motions are translated very quickly into a basis set element that is of crucial importance to hawks: the “prey” element.

In short, objects that fly and flap wings are very near the top of a hawk’s Top Ten List of things to attack when hungry.

And for those of you who were wondering “what in great googly-mooglies does _any_ of this have to do with top ten lists?”, there’s my answer, for better or worse. While seemingly trivial in form, top-ten lists resonate with how as intelligent, efficiency-focused biological systems we set up our fuzzy, flexible, always-adaptable version of basis sets for helping to identify what is important and what is not. They don’t always give the right information, of course, but they give a _start_ at finding the right information, especially if they are derived from a good, deep analysis by some other organism or suite of organisms capable of deriving good importance heuristics.

So, while treating them as sacred is almost always a bad idea, treating well-formulated top-ten lists as a start point for how to handle data overload is not such a bad idea.

So, Dr Carroll… any comments on any of this from your physics perspective?

This will likely be my last entry for a long while in this blog in any case, as I see no point in filling up a blog with long entries of no real interest to the blog owner — and to be honest, I find that I usually end up making long entries in response to anything I find to be worth replying too…

http://andromedachild.blogspot.com/ Andy Fleming

Go on then, Sean… I’ve clicked all the buttons to help make you a cosmic box office internet phenomenon!

yogi-one

I avoid top 10 lists like the plague.

Especially on the internet, because usually what happens is you get sucked in by something like “top 10 bikini celebs of all time” and you are really lusting after that picture of Racquel Welch from that really bad caveman movie she was in where she wears that totally hot animal-skin bikini

BUT – what you get is an introductory page. So you click and load #10 -it isn’t what you are looking for

and since every items on the list is on a separate html page, you have to click for every single item on the list

AND of course the reason for this is so that they can make you look at 10 times as many advertisements as you normally would to view an actual ordered list of 10 items.

AND at least half of those are pop-up ads that you have to shut down before the next page even loads

AND by then, it’s just to hell with it.

spyder

Lists, of any form, are part of the way in which the conscious mind organizes information. We like them because we are hard-wired to like them. The very nature of the internet(s) it/themselves is predicated on lists and long strings of 0/1 and so forth. So what?

Brad H

It is getting better but Discover’s blog software has a long way to go. The content is great but getting at archived data is quite difficult. I wrote your webmaster around a year ago with some suggestions but never received a reply. For all I know, they never even got the e-mail.

Maybe the recent losses at ‘The Intersection’ has spurred some forward progress?

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Cosmic Variance

Random samplings from a universe of ideas.

About Sean Carroll

Sean Carroll is a Senior Research Associate in the Department of Physics at the California Institute of Technology. His research interests include theoretical aspects of cosmology, field theory, and gravitation. His most recent book is The Particle at the End of the Universe, about the Large Hadron Collider and the search for the Higgs boson.
Here are some of his favorite blog posts, home page, and email: carroll [at] cosmicvariance.com .